## AA 174A: Principles of Robot Autonomy I (AA 274A, CS 237A, EE 260A)

Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites:
CS 106A or equivalent,
CME 100 or equivalent (for linear algebra), and
CME 106 or equivalent (for probability theory).

Terms: Aut
| Units: 3-4

Instructors:
Pavone, M. (PI)
;
Bylard, A. (TA)
;
Ivanovic, B. (TA)
...
more instructors for AA 174A »

Instructors:
Pavone, M. (PI)
;
Bylard, A. (TA)
;
Ivanovic, B. (TA)
;
Lee, J. (TA)
;
Migimatsu, T. (TA)
;
Sharma, A. (TA)

## AA 174B: Principles of Robot Autonomy II (AA 274B, CS 237B, EE 260B)

This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implementnthem on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent,
CME 100 or equivalent (for linear algebra),
CME 106 or equivalent (for probability theory), and AA 171/274.

Terms: Win
| Units: 3-4

## AA 274A: Principles of Robot Autonomy I (AA 174A, CS 237A, EE 260A)

Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites:
CS 106A or equivalent,
CME 100 or equivalent (for linear algebra), and
CME 106 or equivalent (for probability theory).

Terms: Aut
| Units: 3-4

Instructors:
Pavone, M. (PI)
;
Bylard, A. (TA)
;
Ivanovic, B. (TA)
...
more instructors for AA 274A »

Instructors:
Pavone, M. (PI)
;
Bylard, A. (TA)
;
Ivanovic, B. (TA)
;
Lee, J. (TA)
;
Migimatsu, T. (TA)
;
Sharma, A. (TA)

## AA 274B: Principles of Robot Autonomy II (AA 174B, CS 237B, EE 260B)

This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implementnthem on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent,
CME 100 or equivalent (for linear algebra),
CME 106 or equivalent (for probability theory), and AA 171/274.

Terms: Win
| Units: 3-4

## BIOE 42: Physical Biology

BIOE 42 is designed to introduce students to general engineering principles that have emerged from theory and experiments in biology. Topics covered will cover the scales from molecules to cells to organisms, including fundamental principles of entropy, diffusion, and continuum mechanics. These topics will link to several biological questions, including DNA organization, ligand binding, cytoskeletal mechanics, and the electromagnetic origin of nerve impulses. In all cases, students will learn to develop toy models that can explain quantitative measurements of the function of biological systems. Prerequisites:
MATH 19, 20, 21
CHEM 31A, B (or 31X),
PHYSICS 41; strongly recommended:
CS 106A,
CME 100 or
MATH 51, and
CME 106; or instructor approval.

Terms: Spr
| Units: 4
| UG Reqs: WAY-AQR, WAY-SMA

Instructors:
Bryant, Z. (PI)
;
Huang, K. (PI)
;
Aris, K. (TA)
;
Ierokomos, A. (TA)
;
Starr, C. (TA)
;
Sun, G. (TA)

## BIOE 102: Physical Biology of Macromolecules

Principles of statistical physics, thermodynamics, and kinetics with applications to molecular biology. Topics include entropy, temperature, chemical forces, enzyme kinetics, free energy and its uses, self assembly, cooperative transitions in macromolecules, molecular machines, feedback, and accurate replication. Prerequisites:
MATH 19, 20, 21;
CHEM 31A, B (or 31X); strongly recommended:
PHYSICS 41,
CME 100 or
MATH 51, and
CME 106; or instructor approval.

Last offered: Winter 2019
| UG Reqs: WAY-AQR, WAY-SMA

## CEE 362A: Uncertainty Quantification (ME 470)

Uncertainty is an unavoidable component of engineering practice and decision making. Representing a lack of knowledge, uncertainty stymies our attempts to draw scientific conclusions, and to confidently design engineering solutions. Failing to account for uncertainty can lead to false discoveries, while inaccurate assessment of uncertainties can lead to overbuilt engineering designs. Overcoming these issues requires identifying, quantifying, and managing uncertainties through a combination of technical skills and an appropriate mindset. This class will introduce modern techniques for quantifying and propagating uncertainty and current challenges. Emphasis will be on applying techniques in genuine applications, through assignments, case studies, and student-defined projects. Prerequisite: Basic probability and statistics at the level of
CME 106 or equivalent.

Terms: Win
| Units: 3

Instructors:
Gorle, C. (PI)

## CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C)

Probability: random variables, independence, and conditional probability; discrete and continuous distributions, moments, distributions of several random variables. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields. Prerequisite:
CME 100/ENGR154 or
MATH 51 or 52.

Terms: Win, Sum
| Units: 4
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Khayms, V. (PI)

## CME 240: Statistical and Machine Learning Approaches to Problems in Investment Management (MS&E 445)

This course will approach a variety of problems in investment management, using statistical and machine learning tools to model forecasting problems in the evolution of security prices. Through a combination of lectures and projects, we will investigate pricing and risk models ranging from individual securities up through asset classes. Occasional guest lecturers will present problems they currently face in their day to day work. Prerequisites: Basic background in Probability (e.g.:
CME 106) and Mathematical Finance (e.g.:
MATH 238), and some facility programming in R and/or Python.

Terms: Spr
| Units: 3

Instructors:
Evnine, J. (PI)

## CME 241: Reinforcement Learning for Stochastic Control Problems in Finance (MS&E 346)

This course will explore a few problems in Mathematical Finance through the lens of Stochastic Control such as Portfolio Management, Optimal Exercise of Derivatives, Order Execution, Personal Finance. For each of these problems, we formulate a suitable Markov Decision Process (MDP), develop Dynamic Programming (DP) solutions, and explore Reinforcement Learning (RL) algorithms. The course emphasizes the theory of DP/RL as well as modeling the practical nuances of these finance problems, and strengthening the understanding through plenty of coding exercises of the methods. Prerequisites: basic background in Probability (eg:
CME 106) and Mathematical Finance (eg:
MATH 238), and some experience coding in Python; Dynamic Programming or Reinforcement Learning experience not required.

Terms: Win
| Units: 3

Instructors:
Rao, A. (PI)

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